How to get a job as a data scientist: It starts with skills

Skills you need to become a data scientist

The journey into data science starts with learning requisite skills and building knowledge. Pursue academic avenues to build a foundation and apply what you learn to build a portfolio of work that might impress potential employers.

A well-rounded data scientist must develop four different types of technical skills to effectively perform their duties:

• Analytical skills to interpret findings from data analyses.
• Computer skills to write code, being familiar with a variety of programming languages and developing algorithms.
• Math and logic skills to build familiarity with many types of statistical models that collect, organize and analyze data.
• Problem-solving skills to troubleshoot issues that might arise when designing algorithms and errors that can occur during data analyses.

Data scientists need to foster their soft skills, such as communication, in addition to technical proficiency. They must share the insights they glean from data analysis with co-workers -- who might lack the same level of technical knowledge and skill -- in easy-to-understand language and digestible visualizations.

The best data scientists work on communicating with others about their findings, creating effective data visualizations and giving presentations for diverse audiences. Decision-makers must understand insights to take effective actions.

To qualify for the role and perform effectively, you'll need to build foundational technical data science skills.

Programming and coding

Python is the most popular programming language used in data science. It's a general-use, high-level language and a variety of free and paid resources are available to begin learning it regardless of skill level. Starting with Python is a good way to build coding skills. After learning it, you can move on to other programming and querying languages, such as R, SQL, Java, C/C++, SAS and more. Two standouts include R for statistical analysis and SQL, a special-purpose programming language that makes it easy to manage data in databases.

Statistics and mathematics

Learning statistics helps you understand how to use equations and statistical models to analyze data and interpret complex and diverse data sets. It's a key skill for extracting insights, making accurate predictions and arriving at informed conclusions. If you're a student, enroll in classes for statistics, calculus, algebra and probability to build a strong mathematical foundation. You can also take individual courses online that dive deeper into concepts such as probability theory, statistical analysis, computer science and cloud computing.

Data preparation, manipulation and analysis

Data preparation and mining are essential data science skills. You must know how to source, organize and clean data. Quality data is key for manipulation and analysis, including model deployment and exploratory data analysis. Familiarize yourself with big data and learn more advanced data engineering techniques to expand your skill set.

Data visualization

As the data expert, you must look at your findings and understand them at a glance, then distill insights into a form that the rest of the business can digest. To convey your insights, you need to create data visualizations. Consider taking a course that breaks down how to tell a story with data or read up on best practices online and practice making visualizations with a data visualization tool.

AI and machine learning

AI simplifies data science in numerous ways. Using AI to its full potential requires understanding of the basics of the technology. It's also a field that will require continuous learning, as it is rapidly evolving. Key concepts to know include linear regression, decision trees, clustering techniques, machine learning (ML), deep learning, algorithm writing, predictive models, neural networks and natural language processing (NLP). Understanding the nuances of AI and ML improves your ability to extract insights, detect patterns and make accurate forecasts.

Data science career opportunities

Data science skills open up a variety of career possibilities beyond the data scientist role. This list is not exhaustive, but it shows the opportunity for vertical and horizontal career growth that's possible with data science skills:

• Data scientists source, clean, organize and analyze data to deliver business insights. The role often has junior titles, such as associate and assistant data scientists, and senior titles.
• Data analysts focus on analyzing data and preparing reports for business users. It's a less technical role compared to a data scientist. It's sometimes posted under business analyst.
• Data architects are responsible for building data platforms, database systems and analytics tools.
• Database administrators and developers maintain, organize and back up database environments.
• Data engineers focus on data pipelines and making delivery more efficient. You might see Level I, II and III data engineer roles, which distinguish seniority. BI engineer or developer are similar titles that focus on BI tasks.
• Chief data officers are the leaders of the data organization. They focus on adhering to data governance, designing data strategy and using data to meet business goals. Other executive-level roles include director, vice president and senior vice president of data.
• Data science managers oversee direct reports in the data science department and help nurture their skill development and growth.
• Data science team leaders manage multiple direct reports, who might have direct reports of their own. They also implement new initiatives, balance workloads and prioritize tasks to meet department-wide deadlines.
• Data visualization specialists use and design digital tools to present complex data to business users.
• AI/ML/NLP specialists are niche specializations in data science. They are engineers and research scientists who focus on honing their area of expertise and using cutting-edge techniques to enhance data use within the organization.
• Applied scientists focus on data analysis to discover practical solutions to real-world problems, often working in industries such as healthcare or engineering.
• Statisticians use statistical techniques and logic to manipulate data and derive actionable insights for the wider organization.
• Data strategists develop high-level data strategies that align with business objectives and foster a data-driven culture.
• Data product managers develop data-centric products and manage their lifecycles to serve data users across the organization.
• Data operations engineers design, refine and maintain data pipelines and data flow throughout an organization to enhance data availability.
• Data translators bridge the gap between data scientists and business leaders. They specialize in communicating and distilling insights to non-technical audiences.
• Data privacy/AI ethics officers focus on adhering to data privacy regulations and navigating ethical uncertainties related to data use.

How to become a data scientist

Education is one of the primary ways to develop the skills you might lack, but if time or money is an issue, you can try alternatives to a traditional degree.

Earn a degree

According to the BLS, data scientists usually need at least a bachelor's degree. The most relevant fields of study for data scientists are mathematics, statistics, computer sciences, business and engineering. If you don't have a degree in those fields, industry-related experience or another type of relevant education, such as a certification, can be acceptable to employers.

For senior-level positions, a master's or doctoral degree is often required, but not mandatory. Earning one helps demonstrate your data science expertise and separates you from the pack. Consider engaging leaders within your organization to see if they can help fund your continuing education and alleviate part of the financial burden.

Get a certification

Outside of a degree, you can pursue bootcamps, courses and certifications to learn data science skills. The value of each varies based on your needs.

For example, entry-level and foundational certifications exist for aspiring data scientists, but are more often geared toward specific skills, specializations or technologies. For example, Amazon Web Services offers AWS certifications that help data engineers become familiar with its cloud platform or dive deeper into cutting-edge technologies like machine learning. Other large platforms, such as Microsoft Azure and Google Cloud, offer similar certifications.

Certifications can bolster your foundational knowledge of a specific platform, which is helpful if your organization uses that platform. If you're new to data science, consider taking more generalized courses before targeting specific platforms.

You can also take individual courses to brush up on or learn certain skills without committing to a degree's full course load. Online courses have free or paid options, but some might have other prerequisites. For example, DataCamp offers an expansive library of courses for individuals in data science, from the basics to more advanced skills.

A variety of data science bootcamps also exist, offering similar education to a degree but in a condensed, more intensive timeline. Courses often take several weeks rather than months or years to complete. It's an approach typically more appropriate for someone switching careers or looking to gain skills at an accelerated pace. New learners might find a traditional degree experience offers a more comprehensive education at a more suitable pace.

Whichever path you choose to take, consider your circumstances and needs. Ask yourself what makes the most sense for your career, seek out programs provided by reputable institutions and make sure you'll receive tangible benefits, such as hands-on guidance or practical projects that build your resume or portfolio. The final factor to keep in mind is cost -- degrees and bootcamps can cost thousands of dollars.

Build a portfolio

Data does all the talking in the data science field. Potential employers want to see how you think about and solve problems and how you communicate findings. The best way to showcase your skills and past successes is to build a portfolio of projects that's easy to share and view.

A strong portfolio should feature projects that address real-world scenarios and offer practical insights. It doesn't need to contain every project you've worked on. Best practices recommend highlighting the most relevant projects and including diverse examples to show a range of skills. Employers often want to see a focus on business results, so you should show the tangible outcomes of your projects.

A portfolio is vital if you don't have any prior experience in the field. It should include any projects from school or bootcamps. If you have experience in other roles, include projects that show how you used data to make decisions.

You can create a data science portfolio on websites such as GitHub, which hosts public repositories of portfolios, or you can create a personal website. Link the portfolio directly on your resume, so hiring managers can pull it up easily, especially during an interview. Make sure you clearly organize projects, including file descriptions, code samples, data visualizations and the results. An organized portfolio is a good way to show how you communicate the value of successful tasks.

Engage with the community

Data science is a rapidly evolving field. You should tap into data science communities to keep up with new techniques and best practices as they develop. Follow thought leaders who share their experiences online to familiarize yourself with the trends and topics that professionals are discussing. You can go the extra mile and engage other users in online spaces by posting your own thoughts on social media sites or in blog posts on your website.

LinkedIn simplifies networking. Make sure to connect with other aspiring data scientists you meet at bootcamps, in school, at conferences, networking events and at work, especially if you've made good connections during an internship. You should be discerning about who you connect with -- the best network is one built with people you trust.

Making connections is one of the best ways to learn about new opportunities. You never know who might post an opening on LinkedIn, providing you a chance to put a foot in the door before other applicants. As people witness your work ethic and trust your results, you'll strengthen your network and its value. Eventually, you can return the favor to other aspiring data scientists by posting openings at your organization when the opportunity arises.

Being active online and having a networking presence is about more than furthering your own career -- it's about learning from others, sharing your knowledge and fostering a stronger data science industry. Community engagement can earn you recognition and respect.

Seek out internships

Nothing stands out more than real-world experience. The better you can define your experience and show the value you brought to an organization, the better you'll be able to capture a hiring manager's attention. If you're a student, try to seek out an internship in the field.

Job boards like LinkedIn and Indeed are good starting points. For students, Handshake is a useful resource -- it works directly with universities to pair students with relevant internships at some of the biggest companies around the world. For those with prior working experience, look for entry-level jobs or part-time roles to get your foot in the door.

Students can also gain relevant professional experience by participating in capstone projects and fellowships, so make sure to ask your university about any opportunities.

Even if your experience is not entirely data science-focused, you can show how you applied a related skill in your job or at school. Connecting skills to other activities shows your understanding of the data scientist role and responsibilities.

Jacob Roundy is a freelance writer and editor with more than a decade of experience with specializing in a variety of technology topics, such as data centers, business intelligence, AI/ML, climate change and sustainability. His writing focuses on demystifying tech, tracking trends in the industry, and providing practical guidance to IT leaders and administrators.